Cost: Productivity Debt & Lost Insights
What do the tool-wrangling, data-grappling, information-hoarding, and all the resulting productivity gaps along the way eventually cost us? For me, the costs fall roughly into two Categories when it comes to Insights: Productivity Debt and Lost Insights.
Productivity Debt
Many of you will be familiar with the term Technical Debt. Ward Cunningham, who coined the term and the concept, says:
“Technical Debt includes those internal things that you choose not to do now, but which will impede future development if left undone. This includes deferred refactoring.” (more here and it’s brilliant)
I’m pretty sure I didn’t make up the term Productivity Debt, but there’s not much written on it to cite. Here’s how I think about it:
Data-wrangling is costly across every layer of our organizations. It’s particularly expensive when senior leaders are doing it, as their time costs the company more than a more junior team member. But often unseen to exec teams are the number of hours invested wrestling with data, to eventually get some version of the truth the wrangler actually believes in.
I’ve been trying for years to quantify this cost in a way that would make enough sense to move a C-suite or a Board to resource transformative work. Time and again we see too few examples of companies actively trying to modernize. Why is that? They don’t see the ROI that change could bring.
To be fair, it is challenging to quantify the cost of missed opportunities, technical debt, and productivity debt. There are many folks smarter than I am who have done the work, and we’ll let their work stand (which I’ll continue to share in the Read, Watch, Follow section at the bottom of each chapter!).
Yet, even with the good work - the proof these prestigious intellectuals bring to the table - it is still challenging for teams everywhere to convince the C-suite to invest in a healthier approach to all manner of processes.
So let’s add another layer to the “convincing” mentioned above. In addition to looking to the experts to learn what these costs are in theory, try this instead. Mental math. Let’s figure out what this is actually costing your organization.
In the past month, how many of you:
Read the above bullet points again. What are the chances for error along the way? And what does that cost? What is the cost in lost time? To do things repeatedly, only to have to go back and repeatedly fix?
Good heavens, what is the cost in compensation alone when you have a couple of big earners wasting time fiddling with a monster spreadsheet?
Then evaluate the flipside - if those pricey folks weren’t spending their time knee deep in spreadsheets, how much value could they be driving for the business, by leading, selling, marketing, developing, executing?
How crazy is it that Sales leaders have to cajole, beg, and plead for folks to update the CRM? Is that not bananas, that that’s the system we live in?
I’m not even sure how to calculate the potential for built-in errors due to this hopping in and out of apps, the opportunity for errors escalating every step of the way, and what that costs us downstream. It’s horrifying.
^This actually keeps me up at night. Both the lost data and the cost.
As organizations, why are we so bad at understanding the tradeoffs? Is it that the current math around these costs isn’t compelling enough to have C-suites invest in what it would take to do it differently and better? Is it that the ROI of doing this work properly isn’t compelling enough? What would it take for that to change?
How can we be agile, move more quickly, test and improve, and fail fast (what cliches am I missing?), when our processes and data are so flawed? What are we actually delivering to our businesses with this mucked up system?
This is a complex math problem but still worth considering explicitly:
The individual’s time (fully loaded) + the potential downstream errors (how to quantify that ?) + the time they’re not spending doing their actual job (how to quantify that ?) = Productivity cost of sub-optimal data-wrangling.
Lost Insights
When we’re data-wrangling rather than mining for insights, when a multitude of unstructured human touchpoint insights go uncaptured, when teams aren’t strategically mining, or consistently sharing with other teams any insights we *do* have, what does it cost when we miss:
If you’re like me, and so many of our peers around the globe, you could probably fill a book with all the missed opportunities!
When’s the last time any of your four functional areas conferred on what they’re each seeing in their respective data?
Look. I know this is tough to quantify. I’ve tried! But it’s vital that your organization starts to quantify loss, so you have a business case for change.
And if your organization is like so many around the world, you’ll need to continuously put this case in front of your team. Change is not easy. Before you reap the fruits of your labor - you have to plant a lot of seeds!!
I have, at times, berated myself for not being able to quantify this better. And I’ve spent countless hours researching the work of others in search of a clear, easy to understand model for C Suites and Boards. To both my dismay and affirmation, there aren’t a lot of clear models in the market today.
For several years, I’ve valued the construct of what’s going on with the CIO’s digital infrastructure. How it’s a Rube Goldberg puzzle that’s so challenging to modernize. How organizations keep applying Band-Aids rather than addressing the wound itself. It’s also monumentally costly and hard to articulate the cost. For a great example of quantifying that challenge , read (via IEEE) Robert Charette’s work on legacy systems.
How and why we spend trillions to keep old software going
Robert does an excellent job of laying out the complexity of changing, and the monumental costs of not. Legacy structures, processes, mindsets are costing us. And it maps beautifully to the legacy structures of other functional areas as well.
We need more of this work.
